Learning Fine-grained Image Similarity with Deep Ranking

Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images.It has higher learning capability than models based on hand-crafted features. A novel multiscale network structure has been developed to describe the images effectively. An efficient triplet sampling algorithm is proposed to learn the model with distributed asynchronized stochastic gradient. Extensive experiments show that the proposed algorithm outperforms models based on hand-crafted visual features and deep classification models.Comment: CVPR 201

Interaction broadening of Wannier functions and Mott transitions in atomic BEC

Superfluid to Mott-insulator transitions in atomic BEC in optical lattices are investigated for the case of number of atoms per site larger than one. To account for mean field repulsion between the atoms in each well, we construct an orthogonal set of Wannier functions. The resulting hopping amplitude and on-site interaction may be substantially different from those calculated with single-atom Wannier functions. As illustrations of the approach we consider lattices of various dimensionality and different mean occupations. We find that in three-dimensional optical lattices the correction to the critical lattice depth is significant to be measured experimentally even for small number of atoms. Finally, we discuss validity of the single band model.Comment: A co-author(AMD) added, paper lengthened (7 pages, 8 figures now) to extend the description of the method and add discussion of its validit

Screening of specific diagnostic peptides of swine hepatitis E virus

© 2009 Zhao et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Comparison of hepatic arterial infusion chemotherapy with mFOLFOX vs. first-line systemic chemotherapy in patients with unresectable intrahepatic cholangiocarcinoma

Background: Systemic chemotherapy (SC) remains the only first-line treatment for unresectable intrahepatic cholangiocarcinoma (iCCA). Hepatic arterial infusion chemotherapy (HAIC) has been recently proven to be effective in managing hepatocellular carcinoma (HCC). Hence, our study aims to investigate the safety and efficacy of HAIC in treating unresectable iCCA patients.Methods: We reviewed 146 patients with unresectable iCCA who had received HAIC or SC between March 2016 and March 2022 in a retrospective manner. Outcomes of patients and safety were compared between the HAIC and SC groups.Results: There were 75 and 71 patients in the HAIC and SC groups, respectively. The median OS in the HAIC and SC groups was 18.0 and 17.8 months (p = 0.84), respectively. The median PFS in the HAIC and SC groups was 10.8 and 11.4 months (p = 0.59), respectively. However, the HAIC group had significantly longer intrahepatic progression-free survival (IPFS) than the SC group (p = 0.035). The median IPFS in the HAIC and SC groups was 13.7 and 11.4 months, respectively. According to the OS (p = 0.047) and PFS (p = 0.009), single-tumor patients in the HAIC group appeared to benefit more. In addition, the overall incidence of adverse events (AEs) was lower in the HAIC group than that in the SC group.Conclusion: Our study revealed that HAIC was a safe and effective therapeutic regimen for unresectable iCCA with better intrahepatic tumor control when compared to SC. Meanwhile, patients with single tumor were more likely to benefit from HAIC than SC

Large field-of-view pine wilt disease tree detection based on improved YOLO v4 model with UAV images

IntroductionPine wilt disease spreads rapidly, leading to the death of a large number of pine trees. Exploring the corresponding prevention and control measures for different stages of pine wilt disease is of great significance for its prevention and control.MethodsTo address the issue of rapid detection of pine wilt in a large field of view, we used a drone to collect multiple sets of diseased tree samples at different times of the year, which made the model trained by deep learning more generalizable. This research improved the YOLO v4(You Only Look Once version 4) network for detecting pine wilt disease, and the channel attention mechanism module was used to improve the learning ability of the neural network.ResultsThe ablation experiment found that adding the attention mechanism SENet module combined with the self-designed feature enhancement module based on the feature pyramid had the best improvement effect, and the mAP of the improved model was 79.91%.DiscussionComparing the improved YOLO v4 model with SSD, Faster RCNN, YOLO v3, and YOLO v5, it was found that the mAP of the improved YOLO v4 model was significantly higher than the other four models, which provided an efficient solution for intelligent diagnosis of pine wood nematode disease. The improved YOLO v4 model enables precise location and identification of pine wilt trees under changing light conditions. Deployment of the model on a UAV enables large-scale detection of pine wilt disease and helps to solve the challenges of rapid detection and prevention of pine wilt disease

Experimental Investigation of Diagram Equilibria in the Co-Nb-Re Ternary System

Abstract(#br)In this study, the isothermal sections of the Co-Nb-Re ternary system at 1200, and 1300 °C have been experimentally determined combining the means of electron probe microanalysis (EPMA) and x-ray diffraction (XRD). The obtained experimental results showed that: (1) The Laves phase of λ3-Co2Nb (C36) was stable at 1300 °C. The temperature was beyond its stability limit in Co-Nb binary system. (2) The solubility of Re in the λ3 phase was so large that the nearest λ2-Co2Nb (C15) phase was essentially surrounded. (3) The solubility of Re in the μ-Co7Nb6 phase was 34.0 at.% at 1200 °C and 35.2 at.% at 1300 °C, respectively. (4) The liquid phase existed at 1300 °C dissolving about 4.0 at.% Re, but it was..

Trend analysis and age-period-cohort effects on morbidity and mortality of liver cancer from 2010 to 2020 in Guangzhou, China

IntroductionLiver cancer is one of the most common malignant gastrointestinal tumors worldwide. This study intends to provide insight into the epidemiological characteristics and development trends of liver cancer incidence and mortality from 2010 to 2020 in Guangzhou, China.MethodsData were collected from the Cancer Registry and Reporting Office of Guangzhou Center for Disease Control and Prevention. Cross-sectional study, Joinpoint regression (JPR) model, and Age-Period-Cohort (APC) model were conducted to analyze the age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) trend of liver cancer among the entire study period.ResultsThe age-standardized incidence and mortality of liver cancer in Guangzhou showed an overall decreasing trend. The disparity in risk of morbidity and mortality between the two sexes for liver cancer is increasing. The cohort effect was the most significant among those born in 1965~1969, and the risk of liver cancer incidence and mortality in the total population increased and then decreased with the birth cohort. Compared with the birth cohort born in 1950~1954 (the reference cohort), the risk of liver cancer incidence and mortality in the males born in 1995~1999 decreased by 32% and 41%, respectively, while the risk in the females decreased by 31% and 32%, respectively.ConclusionsThe early detection, prevention, clinical diagnosis, and treatment of liver cancer in Guangzhou have made remarkable achievements in recent years. However, the risk of liver cancer in the elderly and the middle-aged males is still at a high level. Therefore, the publicity of knowledge related to the prevention and treatment of liver cancer among the relevant population groups should be actively carried out to enhance the rate of early diagnosis and treatment of liver cancer and to advocate a healthier lifestyle

Effects of solar wind density and velocity variations on the Martian ionosphere and plasma transport - a MHD model study

Solar wind dynamic pressure, consisting solar wind density and velocity , is an important external driver that controls Martian plasma environment. In this study, a 3D magnetohydrodynamic model is applied to investigate the separate influences of solar wind density and velocity on the Martian ionosphere. The spatial distributions of ions in the dayside and near nightside ionosphere under different and are analyzed, as well as the ion transport process. We find that for the same dynamic pressure condition, the ionosphere extends to higher altitudes under higher solar wind density, indicating that a solar wind velocity enhancement event is more efficient at compressing the Martian ionosphere. A higher will result in a stronger induced magnetic field, shielding the Martian ionosphere, preventing the penetration of solar wind particles. For the same dynamic pressure, increasing (decreasing ) leads to a higher horizontal ion velocity, facilitating day-to-night plasma transport. As a result, the ionosphere extends farther into the nightside. Also, the ion outflow flux is larger for high , which may lead to a higher escape rate. Moreover, the strong crustal fields in the southern hemisphere also cause significant effect to the ionosphere, hindering horizontal ion transport. An additional outflow channel is also provided by the crustal field on the southern dayside, causing different responses of flow pattern between local and global scale while the solar wind condition is varied

CVPR 2023 Text Guided Video Editing Competition

Humans watch more than a billion hours of video per day. Most of this video was edited manually, which is a tedious process. However, AI-enabled video-generation and video-editing is on the rise. Building on text-to-image models like Stable Diffusion and Imagen, generative AI has improved dramatically on video tasks. But it's hard to evaluate progress in these video tasks because there is no standard benchmark. So, we propose a new dataset for text-guided video editing (TGVE), and we run a competition at CVPR to evaluate models on our TGVE dataset. In this paper we present a retrospective on the competition and describe the winning method. The competition dataset is available at https://sites.google.com/view/loveucvpr23/track4.Comment: Project page: https://sites.google.com/view/loveucvpr23/track